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Active Learning
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Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model
Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning
Bounds on the Generalization Error in Active Learning
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity
Interactive Machine Teaching by Labeling Rules and Instances
Active learning for regression in engineering populations: A risk-informed approach
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching
Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders
From catch-up to frontier: The utility model as a learning device to escape the middle-income trap
ESA: Annotation-Efficient Active Learning for Semantic Segmentation
Understanding Uncertainty-based Active Learning Under Model Mismatch
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling
Causal-Guided Active Learning for Debiasing Large Language Models
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities
Active learning for efficient data selection in radio-signal based positioning via deep learning
Active Learning of Molecular Data for Task-Specific Objectives
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning
Active learning of digenic functions with boolean matrix logic programming
Gravix: Active Learning for Gravitational Waves Classification Algorithms
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection
Robust Offline Active Learning on Graphs
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation
Reciprocal Learning
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model
Hyperbolic Learning with Multimodal Large Language Models
SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms
Active Learning for WBAN-based Health Monitoring
Bayesian Active Learning for Semantic Segmentation
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